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M2LabOrg
/
whisper-small-cs

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Czech
whisper
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use M2LabOrg/whisper-small-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use M2LabOrg/whisper-small-cs with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-cs")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-cs")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-cs")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-small-cs / runs
57.3 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
M2LabOrg's picture
M2LabOrg
Training in progress, step 4000
08c20ee verified almost 2 years ago
  • Jun24_07-29-49_e77a0545485f
    Training in progress, step 1000 almost 2 years ago
  • Jun24_12-55-41_e52c31c1aa4c
    Training in progress, step 4000 almost 2 years ago